Cognitive optical control system and methods

ABSTRACT

A cognitive optical system for dynamically refining imaging during a medical procedure, involving a processor operable by a set of executable instructions storable in relation to a non-transitory memory device. The processor is configured to automatically adjust an image by automatically compensating for at least one external factor affecting an anatomical area being viewed, automatically adjusting at least one imaging parameter, and automatically adjusting at least one internal control of an optical chain, whereby a quality of the image is improvable in real time.

TECHNICAL FIELD

Generally, the present disclosure technically relates to medical imagingsystems. More particularly, the present disclosure technically relatesto control of optical systems for medical imaging systems. Even moreparticularly, the present disclosure technically relates to smartcontrol of optical systems for medical imaging systems.

BACKGROUND

In the related art of surgery, imaging and imaging guidance is becominga more significant component of clinical care, such as relating todisease diagnosis, disease monitoring, surgical approach planning,facilitating guidance during the procedure, and facilitatingpost-operative follow-up, or being a component of a multi-facetedtreatment approach.

Some related art systems involve integration of imaging data in asurgical suite for neurosurgery, wherein brain tumors are typicallyexcised through an open craniotomy approach that is guided by a relatedart imaging device. The related art imaging device typically uses datafrom computerized tomography (CT) scans with an associated contrast(iodinated contrast) feature and magnetic resonance imaging (MRI) scanswith associated contrast (gadolinium contrast). These related artsystems involve registering the imaging data sets together, translatinga three-dimensional imaging space to a three-dimensional space of apatient, tracking instruments relative to the patient, and theassociating imaging data by way of an external hardware system, such asa mechanical arm, a radio-frequency device, or an optical trackingdevice.

These related art systems have experienced many challenges. Forinstance, related art tissue visualization in operating rooms isfrequently hindered by many factors outside control of an optical chain.Specifically, external factors, such as tissue composition and ambientlighting, negatively affect the ability of a user to differentiatevarious types of tissues within a visualized area of a surgical site.Therefore, a need exists for a smart optical control system and methodsto overcome many of the related art challenges.

SUMMARY

In addressing at least many of the challenges experienced in the relatedart, the subject matter of the present disclosure involves a cognitiveoptical control system and methods for dynamically refining imagingduring a medical procedure. In addressing some of the related artchallenges, the cognitive optical control system and methods of thepresent disclosure generally involve optimization of imaging by usingpreviously obtained and real-time information relating to ambientconditions and chemical composition of the tissue at a given surgicalsite. In addition, the cognitive optical control system and methods ofthe present disclosure use previous or “a priori” knowledge, such asprevious or “a priori” information and previous or “a priori” data,relating to at least one factor, such as general anatomy, a patient'sspecific anatomy, geometry of an approach, lighting conditions, type ofsurgical tool, e.g., a pointer, a cutting tool, an aneurysm clip, etc.,a position of a surgical tool, e.g., at or near a surface or a locationat a given depth in a cavity, etc., and the like, to adaptively optimizeat least one of an imaging system, an optical system, a lighting system,or a display system for a given medical or surgical procedure that agiven user, such as a surgeon, is performing.

In accordance with an embodiment of the present disclosure, a cognitiveoptical system for dynamically refining imaging during a medicalprocedure, comprises: a processor operable by a set of executableinstructions storable in relation to a non-transitory memory device andconfigured to automatically adjust an image by: automaticallycompensating for at least one external factor affecting an anatomicalarea being viewed; automatically adjusting at least one imagingparameter; and automatically adjusting at least one internal control ofan optical chain, whereby quality of the image is improvable in realtime.

In accordance with an embodiment of the present disclosure, a method offabricating a cognitive optical system for dynamically refining imagingduring a medical procedure, comprising: providing a processor operableby a set of executable instructions storable in relation to anon-transitory memory device and configured to automatically adjust animage by: automatically compensating for at least one external factoraffecting an anatomical area being viewed; automatically adjusting atleast one imaging parameter; and automatically adjusting at least oneinternal control of an optical chain, whereby quality of the image isimprovable in real time.

In accordance with an embodiment of the present disclosure, a method ofdynamically refining imaging during a medical procedure by way of acognitive optical system, comprising: providing the cognitive opticalsystem, providing the cognitive optical system comprising providing aprocessor operable by a set of executable instructions storable inrelation to a non-transitory memory device and configured toautomatically adjust an image by automatically compensating for at leastone external factor affecting an anatomical area being viewed,automatically adjusting at least one imaging parameter, andautomatically adjusting at least one internal control of an opticalchain, whereby image quality is improvable in real time; automaticallycompensating for at least one external factor affecting an anatomicalarea being viewed; automatically adjusting at least one imagingparameter; and automatically adjusting at least one internal control ofan optical chain, thereby improving quality of the image in real time.

Some of the features in the present disclosure are broadly outlined inorder that the section entitled Detailed Description is betterunderstood and that the present contribution to the art by the presentdisclosure is better appreciated. Additional features of the presentdisclosure are described hereinafter. In this respect, understood isthat the subject matter of the present disclosure is not limited in itsimplementation to the details of the components or steps set forthherein or as illustrated in the several figures of the Drawing, but thesubject matter is capable of being carried out in various ways which arealso encompassed by the present disclosure. Also, understood is that thephraseology and terminology employed herein are for illustrativepurposes in the description and are not regarded as limiting.

BRIEF DESCRIPTION OF THE DRAWING

The above, and other, aspects, features, and advantages of severalembodiments of the present disclosure will be more apparent from thefollowing Detailed Description as presented in conjunction with thefollowing several figures of the Drawing.

FIG. 1 is a diagram illustrating, in a side view, the insertion of anaccess port into a human brain, for providing access to internal braintissue during a medical procedure, such as the NICO™ BrainPath™, inaccordance with an embodiment of the present disclosure.

FIG. 2 is a diagram illustrating, in a perspective view, a surgicalenvironment, such as an operating room, wherein an exemplary navigationsystem to support minimally invasive surgery may be implemented, inaccordance with an embodiment of the present disclosure.

FIG. 3 is a block diagram illustrating a control and processing systemuseable in the navigation system, as shown in FIG. 2, in accordance withan embodiment of the present disclosure.

FIG. 4A is a flow diagram illustrating a method of using the navigationsystem, as shown in FIG. 2, for a surgical procedure, in accordance withan embodiment of the present disclosure.

FIG. 4B is a flow diagram illustrating the step of registering a patientfor a surgical procedure, in the method of using the navigation system,as shown in FIG. 4A, in accordance with an embodiment of the presentdisclosure.

FIG. 5 is a diagram illustrating a perspective view of an implementationof a cognitive optical system for dynamically refining imaging during amedical procedure, in accordance with an embodiment of the presentdisclosure.

FIG. 6 is a schematic diagram illustrating a cognitive optical systemfor dynamically refining imaging during a medical procedure, inaccordance with an embodiment of the present disclosure.

FIG. 7 is a flow diagram illustrating a method of fabricating acognitive optical system for dynamically refining imaging during amedical procedure, in accordance with an embodiment of the presentdisclosure.

FIG. 8 is a flow diagram illustrating a method of dynamically refiningimaging during a medical procedure by way of a cognitive optical system,in accordance with an embodiment of the present disclosure.

Corresponding reference numerals or characters indicate correspondingcomponents throughout the several figures of the Drawing. Elements inthe several figures are illustrated for simplicity and clarity and havenot necessarily been drawn to scale. For example, the dimensions of someelements in the figures are emphasized relative to other elements forfacilitating understanding of the various presently disclosedembodiments. Also, common but well-understood elements that are usefulor necessary in commercially feasible embodiment are often not depictedin order to facilitate a less obstructed view of these variousembodiments of the present disclosure.

DETAILED DESCRIPTION

The systems and methods described herein are useful in the field ofneurosurgery, including oncological care, neurodegenerative disease,stroke, brain trauma, and orthopedic surgery. The subject matter of thepresent disclosure is applicable to other conditions or fields ofmedicine. Noted is that, while the present disclosure describes examplesin the context of neurosurgery, the subject matter of the presentdisclosure is applicable to other surgical procedures that may useintraoperative optical imaging.

Various example apparatuses or processes are below-described. Nobelow-described example embodiment limits any claimed embodiment; andany claimed embodiments may cover processes or apparatuses that differfrom those examples described below. The claimed embodiments are notlimited to apparatuses or processes having all of the features of anyone apparatus or process described below or to features common tomultiple or all of the apparatuses or processes described below. Theclaimed embodiments optionally comprise any of the below-describedapparatuses or processes.

Furthermore, numerous specific details are set forth in order to providea thorough understanding of the disclosure. However, understood is thatthe embodiments described herein are practiced without these specificdetails. In other instances, well-known methods, procedures andcomponents have not been described in detail so as not to obscure theembodiments described herein.

As used herein, the terms, “comprises” and “comprising” are to beconstrued as being inclusive and open ended, and not exclusive.Specifically, when used in the specification and claims, the terms,“comprises” and “comprising” and variations thereof mean the specifiedfeatures, steps or components are included. These terms are not to beinterpreted to exclude the presence of other features, steps orcomponents.

As used herein, the term “exemplary” or “example” means “serving as anexample, instance, or illustration,” and should not be construed aspreferred or advantageous over other configurations disclosed herein.

As used herein, the terms “about”, “approximately”, and “substantially”are meant to cover variations that may exist in the upper and lowerlimits of the ranges of values, such as variations in properties,parameters, and dimensions. In one non-limiting example, the terms“about”, “approximately”, and “substantially” is understood to mean plusor minus 10 percent or less.

Unless defined otherwise, all technical and scientific terms used hereinare intended to have the same meaning as commonly understood by one ofordinary skill in the art. Unless otherwise indicated, such as throughcontext, as used herein, the following terms are intended to have thefollowing meanings:

As used herein, the phrase “access port” refers to a cannula, conduit,sheath, port, tube, or other structure that is insertable into asubject, in order to provide access to internal tissue, organs, or otherbiological substances. In some embodiments, an access port may directlyexpose internal tissue, for example, via an opening or aperture at adistal end thereof, and/or via an opening or aperture at an intermediatelocation along a length thereof. In other embodiments, an access portmay provide indirect access, via one or more surfaces that aretransparent, or partially transparent, to one or more forms of energy orradiation, such as, but not limited to, electromagnetic waves andacoustic waves.

As used herein the phrase “intraoperative” refers to an action, process,method, event or step that occurs or is carried out during at least aportion of a medical procedure. Intraoperative, as defined herein, isnot limited to surgical procedures, and may refer to other types ofmedical procedures, such as diagnostic and therapeutic procedures.

Referring to FIG. 1, this diagram illustrates, in a side view, theinsertion of an access port into a human brain, for providing access tointernal brain tissue during a medical procedure, in accordance with anembodiment of the present invention. An access port 12 is inserted intoa human brain 10, providing access to internal brain tissue. The accessport 12 may include such instruments as catheters, surgical probes, orcylindrical ports, such as the NICO™ BrainPath™. Surgical tools andinstruments may then be inserted within the lumen of the access port inorder to perform surgical, diagnostic, or therapeutic procedures, suchas resecting tumors, as necessary. The present disclosure appliesequally well to catheters, deep brain stimulation (DBS) needles, abiopsy procedure, and also to biopsies and/or catheters in other medicalprocedures performed on other parts of the body. In the example of aport-based surgery, a straight or linear access port 12 is typicallyguided down a sulcal path of the brain. Surgical instruments would thenbe inserted down the access port 12.

Referring to FIG. 2, this diagram illustrates, in a perspective view, anavigation system environment 200, wherein an exemplary medicalnavigation system 205 for supporting minimally invasive accessport-based surgery is implemented, in accordance with an embodiment ofthe present disclosure. The exemplary navigation system environment 200may be used to support navigated image-guided surgery. A surgeon 201conducts a surgery on a patient 202 in an operating room (OR)environment. A medical navigation system 205 comprising an equipmenttower (not shown), a tracking system 321 (FIG. 3), displays 311 andtracked instruments 360 assist the surgeon 201 during his procedure. Anoperator 203 is also present to operate, control and provide assistancefor the medical navigation system 205. The tracked instruments 360 maybe calibrated by way of the calibration and methods as presentlydisclosed.

Referring to FIG. 3, this block diagram illustrates a control andprocessing system 300 operable in the medical navigation system 200,e.g., as part of the equipment tower, in accordance with an embodimentof the present disclosure. In one example, control and processing system300 may include one or more processors 302, a memory 304, a system bus306, one or more input/output interfaces 308, a communications interface310, and storage device 312. Control and processing system 300 may beinterfaced with other external devices, such as tracking system 321,data storage 342, and external user input and output devices 344, whichmay include, for example, one or more of a display, keyboard, mouse,sensors attached to medical equipment, foot pedal, and microphone andspeaker. Data storage 342 may be any suitable data storage device, suchas a local or remote computing device, e.g. a computer, hard drive,digital media device, or server, having a database stored thereon. Inthe example shown in FIG. 3, data storage device 342 includesidentification data 350 for identifying one or more medical instruments360 and configuration data 352 that associates customized configurationparameters with one or more medical instruments 360. Data storage device342 may also include preoperative image data 354 and/or medicalprocedure planning data 356. Although data storage device 342 is shownas a single device in FIG. 3, understood is that in other embodiments,data storage device 342 may be provided as multiple storage devices.

Still referring to FIG. 3, the medical instruments 360 are identifiableby control and processing unit 300. The medical instruments 360 may beconnected to and controlled by control and processing unit 300, ormedical instruments 360 may be operated or otherwise employedindependent of control and processing unit 300. Tracking system 321 maybe employed to track one or more of medical instruments 360 andspatially register the one or more tracked medical instruments to anintraoperative reference frame. For example, medical instruments 360 mayinclude tracking spheres that may be recognizable by a tracking camera307 and/or tracking system 321. In one example, the tracking camera 307may be an infrared (IR) tracking camera. In another example, a sheathplaced over a medical instrument 360 may be connected to and controlledby control and processing unit 300.

Still referring to FIG. 3, the control and processing unit 300 may alsointerface with a number of configurable devices, and mayintraoperatively reconfigure one or more of such devices based onconfiguration parameters obtained from configuration data 352. Examplesof devices 320, as shown in FIG. 3, include one or more external imagingdevices 322, one or more illumination devices 324, a robotic arm 305,one or more cameras 307, one or more projection devices 328, and one ormore displays 311.

Still referring to FIG. 3, exemplary aspects of the disclosure can beimplemented via processor(s) 302 and/or memory 304. For example, thefunctionalities described herein can be partially implemented viahardware logic in processor 302 and partially using the instructionsstored in memory 304, as one or more processing modules or engines 370.Example processing modules include, but are not limited to, a userinterface engine 372, a tracking module 374, a motor controller 376, animage processing engine 378, an image registration engine 380, aprocedure planning engine 382, a navigation engine 384, and a contextanalysis module 386. While the example processing modules are shownseparately in FIG. 3, in one example the processing modules 370 may bestored in the memory 304 and the processing modules may be collectivelyreferred to as processing modules 370.

Still referring to FIG. 3, understood is that the system is not intendedto be limited to the components shown. One or more components of thecontrol and processing system 300 may be provided as an externalcomponent or device. In one example, navigation module 384 may beprovided as an external navigation system that is integrated withcontrol and processing system 300.

Still referring to FIG. 3, some embodiments may be implemented usingprocessor 302 without additional instructions stored in memory 304. Someembodiments may be implemented using the instructions stored in memory304 for execution by one or more general purpose microprocessors. Thus,the disclosure is not limited to a specific configuration of hardwareand/or software.

Still referring to FIG. 3, while some embodiments can be implemented infully functioning computers and computer systems, various embodimentsare capable of being distributed as a computing product in a variety offorms and are capable of being applied regardless of the particular typeof machine or computer readable media used to actually effect thedistribution.

Still referring to FIG. 3, at least some aspects disclosed can beembodied, at least in part, in software. That is, the techniques may becarried out in a computer system or other data processing system inresponse to its processor, such as a microprocessor, executing sequencesof instructions contained in a memory, such as read only memory (ROM),volatile random access memory (RAM), non-volatile memory, cache or aremote storage device.

Still referring to FIG. 3, a computer readable storage medium can beused to store software and data which, when executed by a dataprocessing system, causes the system to perform various methods. Theexecutable software and data may be stored in various places includingfor example ROM, volatile RAM, non-volatile memory and/or cache.Portions of this software and/or data may be stored in any one of thesestorage devices.

Still referring to FIG. 3, examples of computer-readable storage mediainclude, but are not limited to, recordable and non-recordable typemedia such as volatile and non-volatile memory devices, ROM, RAM, flashmemory devices, floppy and other removable disks, magnetic disk storagemedia, optical storage media (e.g., compact discs (CDs), digitalversatile disks (DVDs), etc.), among others. The instructions may beembodied in digital and analog communication links for electrical,optical, acoustical or other forms of propagated signals, such ascarrier waves, infrared signals, digital signals, and the like. Thestorage medium may be the internet cloud, or a computer readable storagemedium such as a disc.

Still referring to FIG. 3, at least some of the methods described hereinare capable of being distributed in a computer program productcomprising a computer readable medium that bears computer usableinstructions for execution by one or more processors, to perform aspectsof the methods described. The medium may be provided in various formssuch as, but not limited to, one or more diskettes, compact disks,tapes, chips, USB keys, external hard drives, wire-line transmissions,satellite transmissions, internet transmissions or downloads, magneticand electronic storage media, digital and analog signals, and the like.The computer useable instructions may also be in various forms,including compiled and non-compiled code.

Still referring to FIG. 3, according to one aspect of the presentapplication, one purpose of the navigation system 205 (FIG. 2), whichmay include control and processing unit 300, is to provide tools to theneurosurgeon that will lead to the most informed, least damagingneurosurgical operations. In addition to removal of brain tumours andintracranial hemorrhages (ICH), the navigation system 205 can also beapplied to a brain biopsy, a functional/deep-brain stimulation, acatheter/shunt placement procedure, open craniotomies,endonasal/skull-based/ENT, spine procedures, and other parts of the bodysuch as breast biopsies, liver biopsies, etc. While several exampleshave been provided, aspects of the present disclosure may be applied toany suitable medical procedure.

Referring to FIG. 4A, this flow diagram illustrates a method 400 ofperforming a port-based surgical procedure by way of using a navigationsystem, such as the medical navigation system 205, as described inrelation to FIG. 2, in accordance with an embodiment of the presentdisclosure. At a first block 402, the port-based surgical plan isimported. Once the plan has been imported into the navigation system atthe block 402, the patient is affixed into position using a body holdingmechanism 404. The head position is also confirmed with the patient planin the navigation system, as indicated by block 404, which in oneexample may be implemented by the computer or controller forming part ofthe equipment tower (not shown). Next, registration of the patient isinitiated, as indicated by block 406. The phrase “registration” or“image registration” refers to the process of transforming differentsets of data into one coordinate system. Data may include multiplephotographs, data from different sensors, times, depths, or viewpoints.The process of “registration” is used in the present application formedical imaging in which images from different imaging modalities areco-registered. Registration is used in order to be able to compare orintegrate the data obtained from these different modalities.

Still referring to FIG. 4A, appreciated is that the present disclosureencompasses numerous registration techniques and at least one of thetechniques may be applied to the present example. Non-limiting examplesinclude intensity-based methods that compare intensity patterns inimages via correlation metrics, while feature-based methods findcorrespondence between image features such as points, lines, andcontours. Image registration methods may also be classified according tothe transformation models used to relate the target image space to thereference image space. Another classification can be made betweensingle-modality and multi-modality methods. Single-modality methodstypically register images in the same modality acquired by the samescanner or sensor type, for example, a series of magnetic resonance (MR)images may be co-registered, while multi-modality registration methodsare used to register images acquired by different scanner or sensortypes, for example in magnetic resonance imaging (MRI) and positronemission tomography (PET). In the present disclosure, multi-modalityregistration methods may be used in medical imaging of the head and/orbrain as images of a subject are frequently obtained from differentscanners. Examples include registration of brain computerized tomography(CT)/MRI images or PET/CT images for tumor localization, registration ofcontrast-enhanced CT images against non-contrast-enhanced CT images, andregistration of ultrasound and CT.

Referring to FIG. 4B, this flow chart illustrates the step ofregistering a patient for a surgical procedure, as indicated by block406, in the method 400 of using the navigation system, as shown in FIG.4A, in greater detail, in accordance with an embodiment of the presentdisclosure. If the use of fiducial touch points 440 is contemplated, themethod involves first identifying fiducial markers on images, asindicated by block 442, then touching the touch points with a trackedinstrument, as indicated by block 444. Next, the navigation systemcomputes the registration to reference markers, as indicated by block446. Of course, the medical navigation system 205 has to know therelationship of the tip of tracked instrument relative to the trackingmarkers of the tracked instrument with a high degree of accuracy for theblocks 444 and 446 to provide useful and reliable information to themedical navigation system 205. An example tracked instrument isdiscussed below with reference to FIG. 5 and a calibration apparatus forverifying and establishing this relationship is discussed below inconnection with FIGS. 6-8.

Still referring to FIG. 4B, alternately, registration can also becompleted by conducting a surface scan procedure, as indicated by block450. The block 450 is presented to show an alternative approach, but maynot typically be used when using a fiducial pointer. First, the face isscanned using a 3D scanner, as indicated by block 452. Next, the facesurface is extracted from MR/CT data, as indicated by block 454.Finally, surfaces are matched to determine registration data points, asindicated by block 456. Upon completion of either the fiducial touchpoints 440 or surface scan 450 procedures, the data extracted iscomputed and used to confirm registration at block 408, shown in FIG.4A.

Still referring to FIG. 4B and referring back to FIG. 4A, onceregistration is confirmed, as indicated by block 408, the patient isdraped, as indicated by block 410. Typically, draping involves coveringthe patient and surrounding areas with a sterile barrier to create andmaintain a sterile field during the surgical procedure. The purpose ofdraping is to eliminate the passage of microorganisms, e.g., bacteria,between non-sterile and sterile areas. At this point, conventionalnavigation systems require that the non-sterile patient reference isreplaced with a sterile patient reference of identical geometry locationand orientation. Numerous mechanical methods may be used to minimize thedisplacement of the new sterile patient reference relative to thenon-sterile one that was used for registration but it is inevitable thatsome error will exist. This error directly translates into registrationerror between the surgical field and pre-surgical images. In fact, thefurther away points of interest are from the patient reference, theworse the error will be.

Still referring to FIG. 4B and referring back to FIG. 4A, uponcompletion of draping, as indicated by block 410, the patient engagementpoints are confirmed, as indicated by block 412, and then the craniotomyis prepared and planned, as indicated by block 414. Upon completion ofthe preparation and planning of the craniotomy, as indicated by block414, the craniotomy is cut and a bone flap is temporarily removed fromthe skull to access the brain, as indicated by block 416. Registrationdata is updated with the navigation system at this point, as indicatedby block 422. Next, the engagement within craniotomy and the motionrange are confirmed, as indicated by block 418. Next, the procedureadvances to cutting the dura at the engagement points and identifyingthe sulcus, as indicated by block 420.

Still referring to FIG. 4B and referring back to FIG. 4A, thereafter,the cannulation process is initiated via the trajectory plan, asindicated by block 424. Cannulation involves inserting a port into thebrain, typically along a sulci path as identified at 420, along atrajectory plan. Cannulation is typically an iterative process thatinvolves repeating the steps of aligning the port on engagement andsetting the planned trajectory, as indicated by block 432, and thencannulating to the target depth, as indicated by block 434, until thecomplete trajectory plan is executed, as indicated by block 424.

Still referring to FIG. 4B and referring back to FIG. 4A, oncecannulation is complete, the surgeon then performs resection, asindicated by block 426, to remove part of the brain and/or tumor ofinterest. The surgeon then decannulates, as indicated by block 428, byremoving the port and any tracking instruments from the brain. Finally,the surgeon closes the dura and completes the craniotomy, as indicatedby block 430. Some aspects, shown in FIG. 4A, are specific to port-basedsurgery, such as portions indicated by blocks 428, 420, and 434, but theappropriate portions of these steps may be skipped or suitably modifiedwhen performing non-port based surgery.

Still referring to FIG. 4B and referring back to FIG. 4A, whenperforming a surgical procedure using a medical navigation system 205,the medical navigation system 205 must acquire and maintain a referenceof the location of the tools in use as well as the patient in threedimensional (3D) space. In other words, during a navigated neurosurgery,there needs to be a tracked reference frame that is fixed relative tothe patient's skull. During the registration phase of a navigatedneurosurgery, as indicated by block 406, a transformation is calculatedthat maps the frame of reference of preoperative MRI or CT imagery tothe physical space of the surgery, specifically the patient's head. Thismay be accomplished by the navigation system 205 tracking locations ofmarkers fixed to the patient's head, relative to the static patientreference frame. The patient reference frame is typically rigidlyattached to the head fixation device, such as a Mayfield clamp.Registration is typically performed before the sterile field has beenestablished, as indicated by block 410.

Referring to FIG. 5, this diagram illustrates, in a perspective view, animplementation of a cognitive optical system S (FIGS. 6-8) fordynamically refining imaging during a medical procedure, in thisexample, neurosurgery. As shown, the area indicated by the dotted linesis designated as a region-of-interest ROI, wherein the processor 10(FIG. 6) of the system S (FIG. 6) is configured to fine tune an opticalchange in relation to parameters, such as color, saturation, brightness,contrast, and the like. The processor 10 is configured to recognize anROI by way of a medical tool, such as a pointer tool, wherein anenhanced image is displayed corresponding to the area indicated by thepointer tool.

Referring to FIG. 6, this schematic diagram illustrates a cognitiveoptical system S for dynamically refining imaging during a medicalprocedure, in accordance with an embodiment of the present disclosure.The system S generally comprises: a processor 10 operable by a set ofexecutable instructions storable in relation to a non-transitory memorydevice (not shown) and configured to automatically adjust an image by:automatically compensating for at least one external factor affecting ananatomical area being viewed; automatically adjusting at least oneimaging parameter; and automatically adjusting at least one internalcontrol of an optical chain, whereby a quality of the image isimprovable in real time.

Still referring to FIG. 6, the system S further comprises at least oneof a camera device or system 20, an optics device or system 30, anillumination device or system 40, a display device or system 50, apreoperative input device 60, an intraoperative input device 70, atleast one external navigation device or system 80 and at least oneadvanced optical or spectroscopic device or system 90, in accordancewith embodiments of the present disclosure. The intraoperative inputdevice 70 is configured to receive input from at least one externalnavigation device or system 80 and at least one advanced optical orspectroscopic device or system 90. Each of the processor 10, the cameradevice 20, the optics device 30, an illumination device 40, and adisplay device 50 is configured to receive input from the preoperativeinput device 60 as well as to receive input from, and transmit outputto, the intraoperative input device 70.

Still referring to FIG. 6, in the system S, the optical chain comprisesat least one of component of optics, mechanical hardware, electronichardware, firmware, and software. The at least one imaging parametercomprises at least one of illumination, saturation, color, contrast, andopacity. The processor 10 is configured to at least one of:automatically adjust illumination by adjusting at least one of anillumination spectrum and a luminance in relation to the camera device20, such as a camera scope, automatically adjust color by adjustingcolor filters in relation to the camera device 20, such as the camerascope, automatically adjust saturation by processing the image to reducelight, and automatically adjust opacity by at least one of adjusting aninfrared illumination level and applying a filter. The processor 10 isconfigured to automatically adjust an image based on at least one inputparameter comprising at least one of a host tissue type, a pathologytype, an environmental condition, an optical chain variable, and aplurality of user experience data, such as via the preoperative inputdevice 60.

Still referring to FIG. 6, in the system S, the processor 10 utilizes amachine learning technique, and/or any other artificial intelligencetechnique, to automatically adjust the image based on the at least oneinput parameter by fine tuning the optical chain. The processor 10 isconfigured to learn from data relating to sources, such as informatics,pathological information, past surgical information, and the like, forfacilitating and/or accelerating the medical procedure, such asneurosurgery. By using the machine learning technique, the processor 10is configured to learn without being explicitly programmed and itsfunctions are not limited by the set of executable instructions. Theprocessor 10 is configured to learn from, and make predictions based on,data, such as past data and real-time data, thereby making data-drivenpredictions, or determinations, e.g., via building a model from sampleinputs, and thereby overcoming strict adherence to the set of executableinstructions. Machine learning is employed by the processor 10 in arange of operations, wherein an explicit set of executable instructionsfor a given operation is infeasible, e.g., in relation to computervision or imaging.

Still referring to FIG. 6, in the system S, the processor 10 utilizes amachine learning technique, involving computational statistics, whichalso focuses in prediction-making, e.g., involving mathematicaloptimization. The machine learning technique may also comprise datamining techniques, involving an exploratory data analysis or anunsupervised learning technique. The machine learning technique may alsoinvolve learning and establishing baseline behavioral profiles forvarious entities or subjects, e.g., patients, and then use the baselinebehavioral profiles to find meaningful anomalies. The exploratory dataanalytics facilitates developing complex models and updatableinstructions for prediction, e.g., via predictive analytics. Theseanalytical models allow the processor 10 to provide medicalprofessionals, such as surgeons, with reliable and repeatable decisionsand to develop insights through learning from historical relationshipsand data trends. The machine learning technique comprises at least onetechnique of: decision tree learning, association rule learning, deeplearning, inductive logic programming, support vector machines,clustering, Bayesian networks, reinforcement learning, representationlearning, similarity and metric learning, sparse dictionary learning,Genetic algorithms, rule-based machine learning, and learningclassifier.

Still referring to FIG. 6, in the system S, the set of executableinstructions comprises a predictive macro-optimization instruction basedon a multi-modal real-time tissue interrogation for facilitatingdynamically refining imaging. The predictive macro-optimizationinstruction comprises informatics, whereby the processor 10 isconfigured to determine at least one ideal condition corresponding tothe at least one external factor. The processor 10 is configured toinstruct an imaging device or system, such as the camera device 20, toprovide a prompt requesting approval of an automated adjustment of theat least one imaging parameter prior to rendering an adjusted image onthe display device 50.

Still referring to FIG. 6, in the system S, the informatics comprises afeature for learning information relating to previous procedures. Theinformation relating to previous procedures comprises at least one typeof imaging parameter for optimizing tissue differentiation. The at leastone internal control of the optical chain comprises at least one of azoom level, a numerical aperture, a camera type, an exposure time, anexposure gain, a de-noising strength, a local area contrast enhancementstrength, a display type, a brightness level, and contrast level.

Still referring to FIG. 6, the cognitive optical system S generallyimproves image quality by automatically adjusting internal controls ofthe optical chain (hardware, firmware, and software) to compensate forexternal factors that affect an area of a surgical site being viewed,whereby a surgeon's ability to view anatomy is improvable, in accordancewith some embodiments of the present disclosure. For example, ambientconditions in the environment surrounding tissue at a surgical site maycause increased illumination, thereby saturating the tissue beingimaged, e.g., when a headlamp is being used. Such additionalillumination is adjustable by way of the cognitive optical system S byadjusting the illumination spectrum and luminance output by the cameradevice 20, e.g., the camera scope, by adjusting colour filters in thecamera scope, and/or by processing the image to reduce the presence ofsuch light. In another example, the cognitive optical system S isimplementable if blood is saturating a field of view (FoV) at a surgicalsite, e.g., automatically detecting whether excess blood is present andmaking adjustments, e.g., automatically adjusting infrared illuminationlevel to reduce opaqueness of the excess blood. Alternatively, thecognitive optical system S uses a filter to reduce the opaqueness.

Still referring to FIG. 6, such adjustments to the optical chain aredynamically performed by the cognitive optical system S; and, in someembodiments, such adjustments to the optical chain are performed inreal-time, whereby visualization of the tissue of interest is constantlybeing re-enhanced. Also, noteworthy is that at least the followingfactors are considered by the cognitive optical system S as adjustableinputs: type of host tissue, type of pathology, ambient and localenvironmental conditions, optical chain variables, information relatingto a plurality of user experiences (or transactions), wherein apredictive macro optimization comprising a dynamic refinement isprovided based on multi-modal real-time tissue interrogation.

Still referring to FIG. 6, the cognitive optical system S, comprisingthe processor 10, is implementable in the context of informatics,wherein the processor 10 learns the ideal conditions relating to a setof specific external factors. For example, glioblastomas or “gliomas”(GBMs) have been imaged, e.g., by way of an imaging system, wherein theideal lighting conditions to best view these gliomas have beendetermined. The cognitive optical system S is implementable with theimaging system to verify whether tissue at a given surgical site has aGBM at any time an external measurement is taken of an imaged area. Inanother example, if a Raman signal indicates that a given portion oftissue indicates at least one of a tumor or a necrotic tissue section,the cognitive optical system S is configured to automatically alter thespectrum of light to maximize differentiability between healthy andunhealthy tissue (such as the tumor or necrotic tissue section) byrendering a boundary more visible therebetween than hitherto possible byusing related art optical systems.

Still referring to FIG. 6, the cognitive optical system S involves anadjustment of parameters, such as incident lighting, via theillumination device 40. However, in implementing some embodiments of thepresent disclosure, wherein factors, such as tissue composition, are notadjustable, the processor 10 is configured to adjust a plurality ofoptical parameters, e.g., for use by the optics device 30, to render atleast one optimized image on the display device 50, wherein the at leastone optimized image comprises at least one of a “true” image of thetissue (as seen by a true source, such as at least one of a naked eyeand a spectroscopic image of viewed tissue), and an enhanced image forfacilitating optimized tissue differentiation, whereby surgicalperformance is improvable.

Still referring to FIG. 6, noteworthy is that any automated adjustmentof imaging parameters should be approved by a surgeon, such as by way ofa prompt from the optical system S prior to rendering the at least oneoptimized image on the display device 50. The optical system S isconfigured to learn information from previous procedures, such as thetypes of imaging parameters that are likely to provide an image whichfacilitates the best or optimized tissue differentiation. For example,if the tissue of interest is preoperatively known by the system S as aglioma, the processor 10 is configured to adjust at least one imagingparameter to acquire an image, whereby imaging of the glioma isoptimized. In another example, the processor 10 is configured toconsider outcomes of previous procedures and to determine what imagingparameters influence better or optimized imaging outcomes. In yetanother example, the processor 10 of the system S is configured toconsider and/or analyze a plurality of “glioma” images taken, e.g., byway of an imaging system, whereby analyzed information is provided, andto cross-correlate such analyzed information with a set of opticalparameters resulting in the best or optimized imaging, e.g., by way ofan imaging system. The processor 10 of the system S uses the set ofoptical parameters, resulting in the best or optimized imaging, toadjust at least one imaging parameter of the optical chain in relationto a given pathology, e.g., a glioma.

Still referring to FIG. 6, when searching for brain tumors, theprocessor 10 of the system S is configured to enhance colour contrast inat least one of the highlights and the mid-tones of an acquired image.In other embodiments, the processor 10 of the system S is configured toadjust other available parameters in relation to a given type ofsurgery. In some embodiments, the processor 10 of the system S isconfigured to use a hierarchal structure for performing any dynamicparameter adjustment. The use of a hierarchical structure is importantin the system S for at least that, in some surgical cases, optimizingone part of the optical chain, after a different optimization hasalready been achieved, may otherwise cause a situation whereinoptimization of one parameter results in a degradation of anotherparameter.

Referring to FIG. 7, and referring back to FIG. 6, this flow diagramillustrates a method M1 of fabricating a cognitive optical system S fordynamically refining imaging during a medical procedure, in accordancewith an embodiment of the present disclosure. The method M1 generallycomprises: providing a processor 10 operable by a set of executableinstructions storable in relation to a non-transitory memory device, asindicated by block 200, and configured to automatically adjust an imageby: automatically compensating for at least one external factoraffecting an anatomical area being viewed, as indicated by block 201;automatically adjusting at least one imaging parameter, as indicated byblock 202; and automatically adjusting at least one internal control ofan optical chain, as indicated by block 203, whereby a quality of theimage is improvable in real time.

Still referring to FIG. 7, and referring back to FIG. 6, in the methodM1, providing the processor 10 comprises configuring the processor 10 toautomatically adjust the at least one internal control of the opticalchain comprising at least one of optical hardware (not shown), opticalfirmware (not shown), or optical software component (not shown).Providing the processor 10 comprises configuring the processor 10 toautomatically adjust the at least one imaging parameter comprising atleast one of illumination, saturation, color, contrast, or opacity, andwherein providing the processor 10 comprises configuring the processor10 to at least one of: automatically adjust illumination by adjusting atleast one of an illumination spectrum or a luminance in relation to acamera device 20, e.g., a camera scope; automatically adjust color byadjusting color filters in relation to the camera device 20, e.g., thecamera scope; automatically adjust saturation by processing the image toreduce light; and automatically adjust opacity by at least one ofadjusting an infrared illumination level or applying a filter.

Still referring to FIG. 7, and referring back to FIG. 6, in the methodM1, providing the processor 10 comprises configuring the processor 10 toautomatically adjust an image based on at least one input parametercomprising at least one of a host tissue type, a pathology type, anenvironmental condition, an optical chain variable, or a plurality ofuser experience data. Providing the processor 10 comprises configuringthe processor 10 as operable by the set of executable instructionscomprising a predictive macro-optimization instruction based on amulti-modal real-time tissue interrogation for facilitating dynamicallyrefining imaging. Providing the processor 10 comprises configuring theprocessor 10 as operable by the set of executable instructionscomprising a predictive macro-optimization instruction comprisinginformatics, whereby the processor 10 is configured to determine atleast one ideal condition corresponding to the at least one externalfactor.

Still referring to FIG. 7 and referring back to FIG. 6, in the methodM1, providing the processor 10 comprises configuring the processor 10 toinstruct an imaging system to provide a prompt requesting approval of anautomated adjustment of the at least one imaging parameter prior torendering an adjusted image on a display device 50. Providing theprocessor 10 comprises configuring the processor 10 as operable by theset of executable instructions comprising a predictivemacro-optimization instruction, the predictive macro-optimizationinstruction comprising informatics, the informatics comprising a featurefor learning information relating to previous procedures, and theinformation relating to previous procedures comprises at least one typeof imaging parameter for optimizing tissue differentiation. The at leastone internal control of the optical chain comprises at least one of azoom level, a numerical aperture, a camera type, an exposure time, anexposure gain, a de-noising strength, a local area contrast enhancementstrength, a display type, a brightness level, or contrast level.

Referring to FIG. 8, and referring back to FIG. 7, this flow diagramillustrates a method M2 of dynamically refining imaging during a medicalprocedure by way of a cognitive optical system, in accordance with anembodiment of the present disclosure. The method M2 generally comprises:providing the cognitive optical system S, as indicated by block 200,providing the cognitive optical system S comprising providing aprocessor 10 operable by a set of executable instructions storable inrelation to a non-transitory memory device (not shown) and configured toautomatically adjust an image by automatically compensating for at leastone external factor affecting an anatomical area being viewed, asindicated by block 201, automatically adjusting at least one imagingparameter, as indicated by block 202, and automatically adjusting atleast one internal control of an optical chain, as indicated by block203, whereby image quality is improvable in real time; automaticallycompensating for at least one external factor affecting an anatomicalarea being viewed; automatically adjusting at least one imagingparameter; and automatically adjusting at least one internal control ofan optical chain, thereby improving quality of the image quality in realtime.

Still referring to FIG. 8, the method M2 further comprises: detectingtemporal noise in an image, as indicated by block 301; determiningwhether the temporal noise exceeds a given threshold, as indicated byblock 302; if the temporal noise fails to exceed the given threshold,detecting temporal noise in an image, as indicated by block 301, or, ifthe temporal noise exceeds the given threshold, determining whetherillumination is occurring at a maximum safe illumination level, asindicated by block 303; increasing illumination to a maximum safeillumination level, as indicated by block 304; determining whether thetemporal noise exceeds the given threshold, as indicated by block 305;if the temporal noise fails to exceed the given threshold, detectingtemporal noise in the image, as indicated by block 301, or, if thetemporal noise exceeds the given threshold, determining whether a zoomlevel is optimized, as indicated by block 306; adjusting the zoom levelto a maximum safe zoom level, as indicated by block 307; determiningwhether the temporal noise exceeds a given threshold, as indicated byblock 308; if the temporal noise fails to exceed the given threshold,detecting temporal noise in an image, as indicated by block 301, or, ifthe temporal noise exceeds the given threshold, determining whether anumerical aperture is optimized, as indicated by block 309; adjustingthe numerical aperture, as indicated by block 310; determining whetherthe temporal noise exceeds a given threshold, as indicated by block 311;if the temporal noise fails to exceed the given threshold, detectingtemporal noise in an image, as indicated by block 301, or, if thetemporal noise exceeds the given threshold, determining whether anexposure time is optimized, as indicated by block 312; adjusting theexposure time level, as indicated by block 313; determining whether thetemporal noise exceeds a given threshold, as indicated by block 314; ifthe temporal noise fails to exceed the given threshold, detectingtemporal noise in an image, as indicated by block 301, or, if thetemporal noise exceeds the given threshold, determining whether anexposure gain is optimized, as indicated by block 315; adjusting theexposure gain level, as indicated by block 316; determining whether thetemporal noise exceeds a given threshold, as indicated by block 317; ifthe temporal noise fails to exceed the given threshold, detectingtemporal noise in an image, as indicated by block 301, or, if thetemporal noise exceeds the given threshold, determining whetherbrightness and contrast are optimized, as indicated by block 318;adjusting the gain level, as indicated by block 319; and re-detectingtemporal noise, as indicated by block 301, in accordance with anembodiment of the present disclosure.

Still referring to FIG. 8, and referring back to FIG. 6, in an exampleof executing the method M2, the system S adaptively modifies power tolower temporal noise, wherein a hierarchical structure is used. Inexecuting the method M2, illumination should be set as high as iscomfortable to a user, considering a distance for which illumination isincreasable without harming the patient. In executing the method M2, thecognitive optical system S considers various parameters in the opticalchain, such as a zoom level and a numerical aperture in relation to theoptical system 30, an exposure time, an exposure gain and de-noisingstrength, and a local area contrast enhancement strength in relation tothe camera system 20, as well as brightness and contrast in relation tothe display device 50. For each parameter, optimal settings may be basedon “a priori” information learned from image drive informatics.

Referring back to FIGS. 1-8, in yet other embodiments of the presentdisclosure, user experience may be obtained and applied by the system Sin executing the method M2 to automate adjustment of variables tooptimize the signal-to-noise ratio (SNR) in entire volume in relation toeither user-selected, or a user-defined region of interest (ROI) withingiven volume segments. The system S considers, not only the opticalchain, but payload information, robotic arm information, and monitorinformation as well. The processor 10 receives input from thepreoperative input device 60 and the intraoperative input device 70,wherein the intraoperative input device 70 receives input from at leastone component, such as the navigation devices or external devices 80 andadvanced optical or spectroscopic devices 90.

Still referring back to FIGS. 1-8, the processor 10 is furtherconfigured to determine whether an image is representative of the actualvolume of view (VoV), e.g., by using image variables, such as tissuetype, e.g., brain tissue, liver tissue, etc., and pathological type, byusing factors that are intrinsic to an image, in comparison with factorsthat are related to the optical chain and with environmental factors,wherein an image can be automatically adjusted with an option of beingmanually overridden if necessary. The set of executable instructionscomprises a macro template set for setting macro conditions.Instructions for interrogation of pathology sets global conditions,e.g., wherein the optical chain, the ambiance, and the room environment,that inform a setting for the intensity and for optimizing gain inrelation to biological materials present, e.g., lipids, etc.

Still referring back to FIGS. 1-8, the processor 10 is furtherconfigured to provide instructions to other system devices forincreasing navigation accuracy, thereby improving acquisition ofincremental data, as the imaging proceeds into the VoV, whereby theoptical chain interrogates the tissue in the VoV in real-time (dynamicinterrogation). By so doing, the system S provides dynamic adjustmentthat is informatics-based in real-time and that is ROI-dependent. Thesystem S also involves a user-defined ROI running in the background,whereby a dynamic automated adjustment of the optical chain andreal-time video processing is performed. The processor 10 is furtherconfigured to provide an instruction for adjusting optical parametersbased on ambiance, biology, pathology, e.g., by determining whether theimage has a correct color contrast and whether the image has a correctSNR based on a given pathology. The processor 10 is further configuredto provide an instruction for effecting micro-adjustments, for changingdimensions, e.g., whether to proceed in the near-infrared (NIR) orwhether to proceed with hyperspectral imaging, whereby an adjusted imageis displayable that better represents an image that is captured by anaked eye. The processor 10 is further configured to provideinstructions for tuning, or fine-tuning, color separation, gamuts, foroptimizing and enhancing contrast, whereby an adjusted image is enhancedbeyond an image that is captured by a naked eye.

Still referring back to FIGS. 1-8, the processor 10 is furtherconfigured to provide instructions for automatically adjustingmagnification after the ROI has been defined, e.g., automaticallyadjusting parameters, such as zoom and working distance, and fordigitally adjusting the camera device 20. The processor 10 is furtherconfigured to provide instructions: for defining the ROI, adjusting afirst parameter, then adjusting the first parameter based on the SNR,for building the hierarchical structure to optimize the SNR, foreffecting micro-adjustments of components, such as optical coherencetomography (OCT), an imaging system, and advanced optics, for providingtissue composition information, for providing feedback as a “truth”source, and for optimizing conspicuity (conspicuousness) of the ROI byperforming iterative interrogations.

Still referring back to FIGS. 1-8, the processor 10 is furtherconfigured to provide instructions: for determining whether an MRIdisplays fat in a tumor at macroscopic level, e.g., by initially usingmacro optics (at the beginning of cases adjusted) based on pathology,for acquiring a specimen, for transmitting the specimen to an imagingsystem, whereby the imaging system provides imaging that indicates ahigh lipid and calcium content, by example only, for transmitting theinformation relating the high lipid and calcium content to an automatedpositioning system, whereby the automated positioning system creates anew micro-environment by adjusting the optical chain via further imageprocessing, whereby the representation of the lipid becomes moreconspicuous, i.e., easier to see, wherein adjusting the optical chainvia further image processing comprises working, adjusting, and usingmulti-modal information, and wherein adjustments are hierarchical.

Still referring back to FIGS. 1-8, in a red environment, e.g., a bloodenvironment, the processor 10 is further configured to provideinstructions for prompting irrigating the red environment with water,wherein determining whether irrigation is necessary comprises using aphotometer). In a surgical procedure, a major challenge in imageprocessing relates to tissue heterogeneity (not all parts of a giventissue appear the same). To address at least this challenge, theprocessor 10 is further configured to provide instructions fordisplaying a dashboard of suggested actions, e.g., Siri for operation,for obtaining inputs from multi-modal sources, for providing output toeffect optical chain video adjustment, for initially tuning allparameters, whereby further image processing is effected only as a lastresort, thereby minimizing the degree of “untruth” in an image.Specifically, in a blood environment, the processor 10 is furtherconfigured to provide instructions for monitoring inputs from all othercomponents, whereby the system S acts as an imaging “watchdog.”

At least some aspects disclosed are embodied, at least in part, insoftware. That is, some disclosed techniques and methods are carried outin a computer system or other data processing system in response to itsprocessor, such as a microprocessor, executing sequences of instructionscontained in a memory, such as ROM, volatile RAM, non-volatile memory,cache or a remote storage device.

A computer readable storage medium is used to store software and datawhich when executed by a data processing system causes the system toperform various methods or techniques of the present disclosure. Theexecutable software and data is stored in various places including forexample ROM, volatile RAM, non-volatile memory and/or cache. Portions ofthis software and/or data are stored in any one of these storagedevices.

Examples of computer-readable storage media may include, but are notlimited to, recordable and non-recordable type media such as volatileand non-volatile memory devices, ROM, RAM, flash memory devices, floppyand other removable disks, magnetic disk storage media, optical storagemedia, e.g., compact discs (CDs), digital versatile disks (DVDs), etc.),among others. The instructions can be embodied in digital and analogcommunication links for electrical, optical, acoustical or other formsof propagated signals, such as carrier waves, infrared signals, digitalsignals, and the like. The storage medium is the internet cloud, or acomputer readable storage medium such as a disc.

Furthermore, at least some of the methods described herein are capableof being distributed in a computer program product comprising a computerreadable medium that bears computer usable instructions for execution byone or more processors, to perform aspects of the methods described. Themedium is provided in various forms such as, but not limited to, one ormore diskettes, compact disks, tapes, chips, USB keys, external harddrives, wire-line transmissions, satellite transmissions, internettransmissions or downloads, magnetic and electronic storage media,digital and analog signals, and the like. The computer usableinstructions may also be in various forms, including compiled andnon-compiled code.

At least some of the elements of the systems described herein areimplemented by software, or a combination of software and hardware.Elements of the system that are implemented via software are written ina high-level procedural language such as object oriented programming ora scripting language. Accordingly, the program code is written in C,C++, J++, or any other suitable programming language and may comprisemodules or classes, as is known to those skilled in object orientedprogramming. At least some of the elements of the system that areimplemented via software are written in assembly language, machinelanguage or firmware as needed. In either case, the program code can bestored on storage media or on a computer readable medium that isreadable by a general or special purpose programmable computing devicehaving a processor, an operating system and the associated hardware andsoftware that is necessary to implement the functionality of at leastone of the embodiments described herein. The program code, when read bythe computing device, configures the computing device to operate in anew, specific and predefined manner in order to perform at least one ofthe methods described herein.

While the present disclosure describes various embodiments forillustrative purposes, such description is not intended to be limited tosuch embodiments. On the contrary, the applicant's teachings describedand illustrated herein encompass various alternatives, modifications,and equivalents, without departing from the embodiments, the generalscope of which is defined in the appended claims. Except to the extentnecessary or inherent in the processes themselves, no particular orderto steps or stages of methods or processes described in this disclosureis intended or implied. In many cases the order of process steps may bevaried without changing the purpose, effect, or import of the methodsdescribed.

Information as herein shown and described in detail is fully capable ofattaining the above-described object of the present disclosure, thepresently preferred embodiment of the present disclosure, and is, thus,representative of the subject matter which is broadly contemplated bythe present disclosure. The scope of the present disclosure fullyencompasses other embodiments which may become obvious to those skilledin the art, and is to be limited, accordingly, by nothing other than theappended claims, wherein any reference to an element being made in thesingular is not intended to mean “one and only one” unless explicitly sostated, but rather “one or more.” All structural and functionalequivalents to the elements of the above-described preferred embodimentand additional embodiments as regarded by those of ordinary skill in theart are hereby expressly incorporated by reference and are intended tobe encompassed by the present claims.

Moreover, no requirement exists for a system or method to address eachand every problem sought to be resolved by the present disclosure, forsuch to be encompassed by the present claims. Furthermore, no element,component, or method step in the present disclosure is intended to bededicated to the public regardless of whether the element, component, ormethod step is explicitly recited in the claims. However, that variouschanges and modifications in form, material, work-piece, and fabricationmaterial detail may be made, without departing from the spirit and scopeof the present disclosure, as set forth in the appended claims, as maybe apparent to those of ordinary skill in the art, are also encompassedby the present disclosure.

INDUSTRIAL APPLICABILITY

Generally, the present disclosure industrially applies to medicalimaging systems. More particularly, the present disclosure industriallyapplies to control of optical systems for medical imaging systems. Evenmore particularly, the present disclosure industrially applies to smartcontrol of optical systems for medical imaging systems.

What is claimed:
 1. A cognitive optical system for dynamically refiningimaging during a medical procedure, comprising: a processor operable bya set of executable instructions storable in relation to anon-transitory memory device and configured to automatically adjust animage by: automatically compensating for at least one external factoraffecting an anatomical area being viewed; automatically adjusting atleast one imaging parameter; and automatically adjusting at least oneinternal control of an optical chain, whereby a quality of the image isimprovable in real time.
 2. The system of claim 1, wherein the opticalchain comprises at least one of optical hardware, optical firmware, andoptical software component.
 3. The system of claim 1, wherein the atleast one imaging parameter comprises at least one of an illumination, asaturation, a color, a contrast, and an opacity, and wherein theprocessor is configured to at least one of: automatically adjust theillumination by adjusting at least one of an illumination spectrum and aluminance in relation to a camera scope, automatically adjust the colorby adjusting at least one color filter in relation to a camera scope,automatically adjust the saturation by processing the image to reducelight, and automatically adjust the opacity by at least one of adjustingan infrared illumination level and applying a filter.
 4. The system ofclaim 1, wherein the processor is configured to automatically adjust theimage based on at least one input parameter comprising at least one of ahost tissue type, a pathology type, an environmental condition, avariable of the optical chain, and a plurality of user experience data.5. The system of claim 1, wherein the set of executable instructionscomprises a predictive macro-optimization instruction based on amulti-modal real-time tissue interrogation for facilitating dynamicallyrefining imaging.
 6. The system of claim 5, wherein the predictivemacro-optimization instruction comprises informatics, whereby theprocessor is configured to determine at least one ideal conditioncorresponding to the at least one external factor.
 7. The system ofclaim 1, wherein the processor is configured to instruct an imagingsystem to provide a prompt requesting approval of an automatedadjustment of the at least one imaging parameter prior to rendering anadjusted image on a display device.
 8. The system of claim 6, whereinthe informatics comprises a feature for learning information relating toprevious procedures.
 9. The system of claim 8, wherein the informationrelating to the previous procedures comprises at least one type ofimaging parameter for optimizing tissue differentiation.
 10. The systemof claim 1, wherein the at least one internal control of the opticalchain comprises at least one of a zoom level, a numerical aperture, acamera type, an exposure time, an exposure gain, a de-noising strength,a local area contrast enhancement strength, a display type, a brightnesslevel, and a contrast level.
 11. A method of fabricating a cognitiveoptical system for dynamically refining imaging during a medicalprocedure, comprising: providing a processor operable by a set ofexecutable instructions storable in relation to a non-transitory memorydevice and configured to automatically adjust an image by: automaticallycompensating for at least one external factor affecting an anatomicalarea being viewed; automatically adjusting at least one imagingparameter; and automatically adjusting at least one internal control ofan optical chain, whereby a quality of the image is improvable in realtime.
 12. The method of claim 11, wherein providing the processorcomprises configuring the processor to automatically adjust the at leastone internal control of the optical chain comprising at least one ofoptical hardware, optical firmware, and optical software component. 13.The method of claim 11, wherein providing the processor comprisesconfiguring the processor to automatically adjust the at least oneimaging parameter comprising at least one of an illumination, asaturation, a color, a contrast, and an opacity, and wherein providingthe processor comprises configuring the processor to at least one of:automatically adjust the illumination by adjusting at least one of anillumination spectrum and a luminance in relation to a camera scope;automatically adjust the color by adjusting at least one color filter inrelation to a camera scope; automatically adjust the saturation byprocessing the image to reduce light; and automatically adjust theopacity by at least one of adjusting an infrared illumination level andapplying a filter.
 14. The method of claim 11, wherein providing theprocessor comprises configuring the processor to automatically adjustthe image based on at least one input parameter comprising at least oneof a host tissue type, a pathology, an environmental condition, avariable of the optical chain, and a plurality of user experience data.15. The method of claim 11, wherein providing the processor comprisesconfiguring the processor as operable by the set of executableinstructions comprising a predictive macro-optimization instructionbased on a multi-modal real-time tissue interrogation for facilitatingdynamically refining imaging.
 16. The method of claim 15, whereinproviding the processor comprises configuring the processor as operableby the set of executable instructions comprising the predictivemacro-optimization instruction, the predictive macro-optimizationinstruction comprising an instruction for using informatics, whereby theprocessor is configured to determine at least one ideal conditioncorresponding to the at least one external factor.
 17. The method ofclaim 11, wherein providing the processor comprises configuring theprocessor to instruct an imaging system to provide a prompt requestingapproval of an automated adjustment of the at least one imagingparameter prior to rendering an adjusted image on a display device. 18.The method of claim 16, wherein configuring the processor as operable bythe set of executable instructions comprises providing a predictivemacro-optimization instruction, the predictive macro-optimizationinstruction comprising an instruction for using informatics, theinstruction for using informatics comprising providing a feature forlearning information relating to previous procedures, and wherein theinformation relating to previous procedures comprises at least one typeof imaging parameter for optimizing tissue differentiation.
 19. Themethod of claim 11, wherein the at least one internal control of theoptical chain comprises at least one of a zoom level, a numericalaperture, a camera type, an exposure time, an exposure gain, ade-noising strength, a local area contrast enhancement strength, adisplay type, a brightness level, and a contrast level.
 20. A method ofdynamically refining imaging during a medical procedure by way of acognitive optical system, comprising: providing the cognitive opticalsystem, providing the cognitive optical system comprising providing aprocessor operable by a set of executable instructions storable inrelation to a non-transitory memory device and configured toautomatically adjust an image by automatically compensating for at leastone external factor affecting an anatomical area being viewed,automatically adjusting at least one imaging parameter, andautomatically adjusting at least one internal control of an opticalchain, whereby image quality is improvable in real time; automaticallycompensating for at least one external factor affecting an anatomicalarea being viewed; automatically adjusting at least one imagingparameter; and automatically adjusting at least one internal control ofan optical chain, thereby improving quality of the image in real time.